Unit testing with JUnit
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Unit testing with JUnit focuses on testing individual units of source code using JUnit. Key aspects covered include: - Unit testing tests the smallest testable parts of code, while integration testing tests groups of code modules and system testing tests complete integrated systems. - Tests in JUnit are implemented as public void testX() methods that call methods under test and assert expected outcomes. - A test suite assembles individual test methods using reflection and executes the suite to automatically run all tests. - Best practices include writing tests before code, testing all code that could break, and running tests frequently for rapid feedback.
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Characterizing and Predicting Which Bugs Get ReopenedThomas Zimmermann This document summarizes a study characterizing and predicting which software bugs get reopened. Through a qualitative survey, researchers identified six main causes of bug reopens: bugs being difficult to reproduce, misunderstood root causes, insufficient bug information, increased bug priority later on, regression bugs, and process-related issues. A quantitative analysis using logistic regression found that bugs found through code analysis tools or human review were less likely to reopen, while bugs found through system or customer testing were more likely to reopen. The analysis also found that bugs opened by people with higher reputations based on past bugs were less likely to reopen.
Data driven games user research
Data driven games user researchThomas Zimmermann This document discusses Microsoft's use of data-driven user research methods for game development. It summarizes different types of data collected, including in-game telemetry, Xbox Live usage data, and online forum discussions. Examples are provided of analyses conducted on data from games like Project Gotham Racing 4 and Halo 3 to understand player behavior and identify underutilized game elements. The goal is to better understand the player experience and Xbox ecosystem to inform game design and personalization.
Not my bug! Reasons for software bug report reassignments
Not my bug! Reasons for software bug report reassignmentsThomas Zimmermann This document discusses reasons for software bug report reassignments in Microsoft products. Through qualitative surveys and analysis of bug reports for Windows Vista, it identifies the top reasons for reassignments as determining the root cause, unclear ownership between teams, poor bug report quality, determining the proper fix, and workload balancing. It also finds that bug reports with reassignment cycles at the beginning are more likely to be fixed, while cycles at the end indicate risk of the bug not being addressed. The lessons learned point to making the bug fixing process more collaborative, fluid and coordinated.
Empirical Software Engineering at Microsoft Research
Empirical Software Engineering at Microsoft ResearchThomas Zimmermann An invited talk that I gave in Tokyo. Very special thanks to Shuji Morisaki who was my translator during the session. Many thanks to Chris Bird, Nachi Nagappan, Rahul Premraj, and Sascha Just who provided slides for this talk.
Security trend analysis with CVE topic models
Security trend analysis with CVE topic modelsThomas Zimmermann This document discusses using topic models to analyze trends in vulnerability data from Common Vulnerabilities and Exposures (CVE). Latent Dirichlet allocation is used to identify topics in CVE summaries from 2000-2009. Key trends identified include a decline in buffer overflows and format strings but an increase in SQL injection, cross-site scripting, and application servers. The topics identified generally align well with the Common Weakness Enumeration classifications.
Analytics for software development
Analytics for software developmentThomas Zimmermann 1) The document discusses software analytics and how it can be used to understand what happened in the past, what is currently happening, and what may happen in the future for software development projects.
2) It notes that different stakeholders like researchers, developers, testers, and managers have different needs for analytics and a single tool may not be enough to meet all needs.
3) The presentation outlines challenges with software analytics including data collection, data quality, privacy, understanding different user needs, and user experience. It stresses the importance of making data actionable and accessible for stakeholders.
Characterizing and predicting which bugs get fixed
Characterizing and predicting which bugs get fixedThomas Zimmermann This document discusses factors that affect whether software bugs get fixed. It analyzes bug report data from Microsoft and surveys Microsoft employees. The analysis found that bugs with one reassignment are more likely to be fixed, but too many reassignments decrease likelihood. Bugs opened by people with high reputations and handled within single teams and locations are more likely to be fixed. The survey revealed social factors like seniority, reputation, relationships, and trust also impact the bug fixing process.
Changes and Bugs: Mining and Predicting Development Activities
Changes and Bugs: Mining and Predicting Development ActivitiesThomas Zimmermann The document discusses mining software archives and bug databases to predict software development activities and defects. It presents two main contributions: 1) fine-grained analysis of version archives to identify usage patterns and cross-cutting changes, and 2) mining bug databases to predict defects based on dependencies and the increased likelihood of defects when depending on defect-prone code.
Cross-project defect prediction
Cross-project defect predictionThomas Zimmermann This document examines cross-project defect prediction, where a model trained on one project is used to predict defects in another project. In an experiment of 622 cross-project combinations across 12 systems, only 3.4% had successful predictions. However, certain project similarities like domain and characteristics like code reviews increased prediction precision and recall. Decision trees were created to help select project combinations where cross-project prediction is more likely to succeed based on characteristics like intended audience and pre-release bug metrics.
Changes and Bugs: Mining and Predicting Development Activities
Changes and Bugs: Mining and Predicting Development ActivitiesThomas Zimmermann 1. The document discusses mining software archives and predicting development activities through analyzing version control data, bug databases, and code dependencies.
2. Key contributions include fine-grained analysis of version archives to identify patterns and cross-cutting changes, and mining bug databases to predict defects based on dependencies and historical data.
3. Future work involves using social networking data and collaboration information to improve defect prediction.
Predicting Defects using Network Analysis on Dependency Graphs
Predicting Defects using Network Analysis on Dependency GraphsThomas Zimmermann Network analysis of dependency graphs between code components can help predict defects and prioritize testing. Analyzing dependencies captured metrics like centrality and ego networks that strongly correlated with past defects. These network measures improved prediction of defects, ranking highest risk binaries, and identification of critical "escrow" binaries requiring extra testing over complexity metrics alone, doubling recall of escrow binaries. Combining network and complexity analyses provides more effective defect prediction and testing prioritization to aid new QA managers.
Quality of Bug Reports in Open Source
Quality of Bug Reports in Open SourceThomas Zimmermann The document discusses research into what makes a good bug report based on a survey of over 150 developers. It finds that the most helpful items for fixing bugs are steps to reproduce, stack traces, and observed behavior. The biggest problems causing delays are incomplete information, wrong steps to reproduce, and wrong expected behavior. The research also measured bug report quality and found stack traces and readability correlated with shorter fix times.
Meet Tom and his Fish
Meet Tom and his FishThomas Zimmermann The document discusses software mining research including predicting bugs, defects, and vulnerabilities. It examines usage patterns and recommends systems to address cross-cutting concerns. The research analyzes past software projects and aims to guide future development.
Predicting Subsystem Defects using Dependency Graph Complexities 
Predicting Subsystem Defects using Dependency Graph Complexities Thomas Zimmermann The document presents a method for predicting subsystem failures in software projects using dependency graph complexities. It finds that code dependency cycles and large cliques (fully connected subgraphs) correlate with more defects. It also finds that measures of complexity in a subsystem's dependency graph, such as the number of nodes and edges, can be used to predict the number of defects in the subsystem. Evaluating the method on a large Microsoft software project showed it could effectively rank subsystems by likelihood of failure.
Got Myth? Myths in Software Engineering
Got Myth? Myths in Software EngineeringThomas Zimmermann The document summarizes a working session on software engineering myths held on October 4, 2007 in Paris. The session included discussions of common myths in areas such as complexity, bugs, clones, aspect-oriented programming, and project management. Participants shared and debated myths in small group discussions. Some examples of myths discussed include the ideas that complexity leads to more bugs, clones are always bad, and aspect-oriented programs are inherently easier to maintain. The session aimed to evaluate whether such perceptions are truly myths or if aspects of them have been found to hold true in various studies.
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